The minimal agent — one tool, four iterations

from anthropic import Anthropic
import json
client = Anthropic()

# One tool
TOOLS = [{
    "name": "get_weather",
    "description": "Get the current weather for a city.",
    "input_schema": {
        "type": "object",
        "properties": {"city": {"type": "string", "description": "City name"}},
        "required": ["city"],
    },
}]

def get_weather(city):
    # call your weather API
    return {"city": city, "temp_c": 22, "conditions": "sunny"}

def run_agent(user_msg, max_steps=4):
    messages = [{"role": "user", "content": user_msg}]
    for step in range(max_steps):
        resp = client.messages.create(
            model="claude-sonnet-4-6",
            max_tokens=800,
            tools=TOOLS,
            messages=messages,
        )
        messages.append({"role": "assistant", "content": resp.content})

        if resp.stop_reason == "end_turn":
            return resp.content[0].text

        # execute every tool_use block
        tool_results = []
        for block in resp.content:
            if block.type == "tool_use":
                if block.name == "get_weather":
                    result = get_weather(block.input["city"])
                else:
                    result = {"error": "unknown tool"}
                tool_results.append({
                    "type": "tool_result",
                    "tool_use_id": block.id,
                    "content": json.dumps(result),
                })
        messages.append({"role": "user", "content": tool_results})

    return "(agent reached max_steps without finishing)"

print(run_agent("What's the weather in Paris and Berlin?"))
# Expected: model calls get_weather twice (often in parallel), then answers
# with both temperatures. ~2 LLM calls + 2 tool calls.